RNA-Seq Methods & March Twitter Roundup

There were lots of interesting developments this month that didn't work their way into a full blog post. Here is an incomplete list of what I've been tweeting about over the last few weeks. But first I want to draw your attention to the latest manuscript for a new bioconductor package for doing RNA-seq in R.

DEXSeq vs Cuffdiff. See the pre-publication manuscript from Simon Anders, Alejandro Reyes, and Wolfgang Huber: "Detecting differential usage of exons from RNA-Seq data." DEXSeq is an R package by the same guys who developed the DESeq R package and the HTSeq python scripts. (Incidentally, both DESeq and DEXSeq are rare examples of bioconductor vignettes which are well developed and are a pleasure to read). I often use cufflinks/cuffdiff in the bioinformatics core was because many other tools and methods only allow you to interrogate differential expression at the gene level. Using cufflinks for transcriptome assembly enables you to interrogate transcript/isoform expression, differential splicing, differential coding output, differential promoter usage, etc. DEXSeq uses similar methodology as DESeq, but can give you exon-level differential expression, without going through all the assembly business that cufflinks does. In one of the supplementary tables in their pre-pub manuscript, they compare several versions of cuffdiff to DEXSeq on two datasets. Both of these datasets had biological replicates for treatment and control conditions. They compared treatment to controls, and found DEXseq gave you more significant hits than cuffdiff. Then they compared controls to other controls (ideally should have zero hits) and found cufflinks had way more hits. See p13, p23, tables S1 and S2.

Proper comparison treatment vs control, # significant hits:

DEXSeq: 159

Cuffdiff 1.1: 145

Cuffdiff 1.2: 69

Cuffdiff 1.3: 50

Mock comparison controls vs controls, # significant hits:

DEXSeq: 8

Cuffdiff 1.1: 314

Cuffdiff 1.2: 650

Cuffdiff 1.3: 639

In the UVA Bioinformatics core we strive for reproducibility, scalability, and transparency using the most robust tools and methodology available. It gives me pause to see such alarmingly different results with each new version and each new protocol of a particular tool. What are your thoughts and experiences with using Cufflinks/Cuffdiff, DESeq/DEXSeq, or the many, many other tools for RNA-Seq (MISO, ExpressionPlot, EdgeR, RSEM, easyRNASeq, etc.)? Please share in the comments.